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Learning the Ideal Evaluation Function

  • Edwin D. de Jong
  • Jordan B. Pollack
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2723)

Abstract

Designing an adequate fitness function requires substantial knowledge of a problem and of features that indicate progress towards a solution. Coevolution takes the human out of the loop by dynamically constructing the evaluation function based on interactions between evolving individuals. A question is to what extent such automatic evaluation can be adequate. We define the notion of an ideal evaluation function. It is shown that coevolution can in principle achieve ideal evaluation. Moreover, progress towards ideal evaluation can be measured. This observation leads to an algorithm for coevolution. The algorithm makes stable progress on several challenging abstract test problems.

Keywords

Coevolution Pareto-Coevolution Complete Evaluation Set ideal evaluation underlying objectives Pareto-hillclimber over-specialization 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Edwin D. de Jong
    • 1
  • Jordan B. Pollack
    • 1
  1. 1.DEMO Lab, Volen National Center for Complex SystemsBrandeis UniversityWalthamUSA

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